Method for mining, mapping and managing organizational knowledge from text and conversation

ABSTRACT

A network text analysis called center resonance analysis (CRA) is presented which represents texts (or transcribed conversations) as networks of centering words. The networks of centering words are components of utterances or (specifically noun phrases) that authors and/or speakers deploy in a manner that makes their utterances coherent. A CRA network can be derived for any text, and abstractly represents its main concepts, their influence, and their interrelationships.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional ApplicationNo. 60/255,834 filed Dec. 15, 2000 which is hereby incorporated byreference.

FIELD OF INVENTION

[0002] The present invention relates generally to a method for analyzingtext using a network. More particularly, the present invention relatesto a method for mining, mapping, and managing organizational knowledgefrom written text and conversation which identifies discursivelyimportant words and represents them collectively as a network.Structural properties of the network are then used to index wordimportance.

BACKGROUND OF THE INVENTION

[0003] Organizational knowledge includes symbols, routines, andresources that are used by an organization's members to coordinateaction and interaction. The management of an organization's knowledge isof great strategic importance to that organization.

[0004] In order to study organizational knowledge, data must be obtainedfrom organizational discourse, namely written texts and conversation.However, once organizational discourse is obtained, the massive amountof data resulting from such discourse must be analyzed.

[0005] Informational technology plays a key role in facilitatingorganizational knowledge and learning via a process of knowledgemapping, mining, and management. Knowledge mining involves the captureof organizational knowledge embedded in text and conversation; knowledgemapping involves representing such knowledge artifacts and their sourceand content in useful ways; and knowledge management involves theapplication of such analyses for organizational benefit. Text analysiscomprises this process of mapping, mining, and managing.

[0006] There are many different approaches to text analysis. Three mainapproaches of text analysis are based on inference, positioning, andrepresentation. Approaches based on inference draw conclusion about whatis not given in the text. Inference approaches apply rules or learnedpatterns to content that is directly given in the text or,alternatively, distinguish important material from unimportant materialusing similar sets of rules. In positioning approaches, abstractprofiles of texts are generated and then positioned using spatialmodeling techniques that are relative to other texts in a set or corpus.Representational approaches produce representations of texts byextracting or distilling its given content without reference to atraining set, corpus, semantic rule set, or field of other documents.The representations are instrinsically meaningful and do not depend onoutside contexts or sources of information. Representational approachesinclude keyword indexes and network text analysis.

[0007] Those approaches based on a concept network are best suited forcapturing discourse by computer. The common theme in network textanalysis is that text can be represented as a network of co-occurrencesof words. The variations are in the details of how the links are formed.In most existing approaches, words are counted as being linked if theyco-occur within some arbitrarily sized “window” of words as it is slidalong the text. Once the text is represented in the form of a network ofconcepts, it is susceptible to a range of powerful analysis techniquesthat can describe the structural properties of particular words and/orthe overall network.

[0008] However, in network text analysis, there is not much consistencyin the ways that researchers represent text as a network. Problemsinclude 1) that the criteria for unitizing the text are seldom wellestablished, and 2) that the networks themselves are not very thoroughlyconceptualized (i.e. researchers do not always address what a link meansin theoretical terms or state exactly what it is that flows through anetwork of words).

[0009] Accordingly, there is a need for a representational network textanalysis approach which 1) is based on a network representation ofassociated words that takes advantage of the complex data structureoffered by that network, 2) represents intentional, discursive acts ofcompetent authors and speakers, and 3) is versatile and transportableacross contexts.

SUMMARY OF THE INVENTION

[0010] The present invention is directed to a method for network textanalysis entitled Central Resonance Analysis (CRA). CRA can be appliedto large quantities of written text, as well as transcribedconversation, and has broad applications. CRA identifies discursivelyimportant words and represents them as a network. Structural propertiesof the network are then used to index word importance. Therefore, thestructuring of word importance is performed without reference to othertexts, corpora, rule sets, training data, etc.

[0011] CRA is a representational method that assumes that competentauthors and/or speakers generate utterances that are locally coherent byfocusing their statements on conversational centers. These centers arenoun phrases that constitute the subjects and objects of utterances. Forexample, each sentence in a written text (except the first sentence) hasa backward looking center that refers to a preferred forward-lookingcenter which is expressed in the previous utterance. CRA codes the waysthat authors deploy centers in order to create a structure of conceptualrelationships in the authors′ text.

[0012] CRA employs four steps to generate a representation of a text.First, a selection step is performed to unitize the centers bygrammatically parsing the utterances. More specifically, noun phrases inthe utterances are identified and extracted from the text for furtheranalysis. One or more nouns and zero or more adjectives make up the nounphrases. In one aspect of the invention, disambiguated nouns may besubstituted for pronouns that represent them when the pronouns arerelevant to the analysis. In another aspect of the invention, minimalaffix stemming is performed to change plural words to their singularforms by removing “s” or “es” suffixes.

[0013] Second, a linking step is performed where the component words(nouns and adjectives) of each noun phrase are sequentially linkedwithin sentences, and all possible co-occurrences of words within largernoun phrases are linked thereby converting word sequences into networksof relationships between words.

[0014] Third, an indexing step is performed in which the network of wordassociations is analyzed to determine the relative structural influenceof each word. Betweenness centrality is used to measure the stucturalinfluence of the words and best represents the extent to which aparticular centering word mediates chains of association in the CRAnetwork.

[0015] Fourth, a mapping step or application is performed where theindexed CRA network, or a set of indexed networks, is used for some typeof analysis task. For example, one such task could be the visualizationof the CRA network in order to understand the content of the text. Otherapplications include, but are not limited to, spatial modeling ofresonance scores, information retrieval, and thematic analysis ofcollections.

[0016] The mutual relevance of one or more texts can be measured byusing resonance. Resonance can be measured based on the common words inone or more documents (word resonance) or it can be based on words pairsthat are common in one or more documents (pair resonance). The more wordresonance two texts have, the more the communicators of the texts usedthe same words, and the more those words were prominent in structuringthe coherence of the text. The more pair resonance two texts have, themore the authors of the texts assembled words in the same ways in orderto make their communication coherent. Resonance scores can be used tosearch or model one or more texts

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is illustrated by way of example and notlimitation in the accompanying figures, in which FIGS. 1-4 depict oneapplication of the network text analysis method of the presentinvention, namely CRA network visualization using a transcribedvideotape of organizational decision making, and FIGS. 5-6 depictanother application of the network text analysis method of the presentinvention, namely spatial modeling used to cluster university faculty inan interdisciplinary research area, and in which:

[0018]FIG. 1 is a table of important statements both supporting andopposing a need for structural change within an organization;

[0019]FIG. 2 is a CRA network of those statements in FIG. 1 supporting aneed for structural change where highest influence words are in black,next highest influence words are in gray, and next highest influencewords are in white with many words lower in influence not shown;

[0020]FIG. 3 is a CRA network of those statements in FIG. 1 opposing aneed for structural change where highest influence words are in black,next highest influence words are in gray, and next highest influencewords are in white with many words lower in influence not shown;

[0021]FIG. 4 is a CRA network of the whole relevant section of thetranscript including the arguments collected and charted in FIG. 1;

[0022]FIG. 5 is a hierarchical cluster analysis using Ward's method foragglomeration of a matrix of similarities between 48 facultyresearchers; and

[0023]FIG. 6 is a hybrid analysis showing the clusters obtained from thecluster analysis in FIG. 5 superimposed on a scatter plot ofmultidimensional scaling results of the similarities between the 48faculty researchers.

DETAILED DESCRIPTION

[0024] The present invention is directed to a network analysis methodfor mining, mapping, and managing organizational knowledge from text andconversation. The network analysis method is entitled Center ResonanceAnalysis (CRA). CRA codes the co-occurrence of words used by authorsand/or speakers to center their utterances, thereby transforming textand transcribed conversation into abstract network representations. CRAdescribes these networks via quantitative metrics and visual maps, andmatches documents and text through an analysis of their resonance.

[0025] In CRA, communication is unitized in terms of words contained inthe noun phrases that make up utterances. Utterances are sentences, orthe conversational equivalent of sentences, which represent finitegroups of centers constructed by communicators to fit into a coherentstream of other utterances or sentences. Noun phrases identify thecenters and the words making up the noun phrases function as linkableunits.

[0026] CRA is concerned with the deployment of a stream of centerswithin utterances. CRA assumes coherent communication by competentwriters and speakers and extracts some of its associated structure. Foursteps are involved in generating a CRA representation of a text. Thesesteps include a selection step which parses utterances into theircomponent noun phrases, a linking step which converts the sequences ofwords contained in noun phrases into networks of relationships betweenwords, an indexing step where the network of word associations isanalyzed to determine the relative influence of each word, and a mappingor application step where the indexed CRA network, or a set of indexedCRA networks, is used for a type of analysis task such as visualizationof the CRA network to understand text, for example. The four steps aredescribed below in more detail.

[0027] Selection

[0028] CRA categorizes texts in terms of a pattern of connectionsbetween words that are crucial to the centering process. Compiling thewords and their connections across all utterances in a text yields a CRAnetwork representing the text. The procedure is in the spirit of earliernetwork text analysis methods, but represents a more restricted form oflinking that takes account of the discursive structure of the utterance.It begins with selection. Rather than linking all words that fall withinan arbitrarily-sized window of text, CRA parses an utterance into itscomponent noun phrases. A noun phrase is a noun plus zero or moreadditional nouns and/or adjectives, which serves as the subject orobject of a sentence. Determiners (the, an, a, etc.), which can also beparts of noun phrases, are dropped in CRA analyses. Thus, a noun phraseis represented by one or more words, and a sentence can consist of oneor more noun phrases. Since the centering process operates largelythrough noun phrases, this step acts as a filter that turns sentencesinto sequenced sets of words contained in noun phrases.

[0029] Before moving to the next step of the CRA method, there are a fewimportant issues to note. First, CRA intentionally excludes the othermain component of utterances, namely verb phrases. In the linguisticmodel underlying CRA, verb phrases would be the “action” componentslinking different noun phrases in an utterance. As such, they are reallya different kind of information, about the contexts of action that linkthe centers. Given the concern to represent the manifest content oftexts, rather than provide inferences about the significance ofparticular utterances in ongoing interaction, the exclusion of verbphrases is logical. Noun phrases, according to linguistic semanticists,are the only elements that can be unambiguously classified as entitiesin discourse. Nouns denote conceptual categories that provide moresalient discourse information than verbs and generally control the useand expression of verb phrases. Moreover, nouns are less likely thanverbs to be temporally situated, and are thus more likely to beportrayed as entities (i.e., concepts) in discourse. In short, theparsing of texts into networks of noun phrases, and the concomitantexclusion of verbs, aligns both a guiding model of discourse coherenceand a representation for the manifest content of texts.

[0030] The inclusion or exclusion of pronouns in CRA is contingent onthe purpose of the investigation and the quantity of texts involved. Inmost spoken and written texts, proper nouns or referents are introducedbefore pronouns and topic shifts are introduced by specific nouns,meaning that little textual information is lost by dropping pronouns (asbackward-looking centers) that appear later. Therefore, CRA can safelyforego disambiguation of pronouns, dropping them from the analysis. Inother cases, the identification of actors pronominalized (by words suchas she, he, it, I, we or they) may be relevant to the analysis. In thosecases, disambiguated nouns may be substituted for the pronouns thatrepresent them.

[0031] Stemming is used to convert words to more basic root forms beforeanalyzing them. However, stemming can obscure important shades ofmeaning. For example, the statements “the negotiators connected on theissues” and “there was a disconnect between the negotiators on theissues” would stem to the same set of objects, despite quite oppositemeanings. Accordingly, CRA adopts only minimal affix stemming, goingfrom plural to singular forms by removing “s” or “es” suffixes.

[0032] Linking

[0033] The second step, linking, converts the word sequences intonetworks of relationships between words. The author or speaker of a textbeing analyzed with CRA intentionally groups the words into noun phrasesand strings these phrases together (using verbs, pronouns, determiners,etc.) to form an utterance. CRA linking rules embody those choices. Allwords comprising the centers in the utterance are linked sequentially.In the majority of cases, where noun phrases contain one or two words,the sequential connections capture all the linkage intended by theauthor; there no higher-order connections are possible without crossingthe boundaries of the noun phrases. However, there are cases where threeor more words are contained in a single noun phrase. Here just thesequential links do not exhaust the connectedness possible in the setcreated by the author. Therefore, all possible pairs of words are linkedwithin the noun phrases. For example the phrase “complex discursivesystem” would generate the links: complex-discursive, discursive-system,and complex-system.

[0034] Accumulating links over a set of utterances comprising a text (ora paper, a collection of papers, a transcribed speaking turn or set ofturns, and so on) yields a symmetric, valued, undirected network whosenodes represent the center-related words. In a CRA network the linkvalues represent the number of times the words were linked in the textaccording to the rules above. This network, when indexed as described inthe next step, becomes a fundamental representation of the text andforms the basis for all applications of CRA.

[0035] Indexing

[0036] The third step in CRA is indexing. Here the network of wordassociations is analyzed to determine the relative influence of eachword (or node). This is a key step in differentiating the words. Networkmetaphors are always based on some abstract notion of flow, and in thecase of CRA networks there is a flow of meaning. To the extent that aCRA network is structured, some words are more influential than othersin channeling flows of meaning. In other words, some words are literallymore meaning-full than other words in the network. Therefore,identifying the structural influence of the words allows one to measurethis property. The idea of influence is operationalized as thecentrality of a given word in the CRA network. Although a variety ofmeasures could be used, centering theory points most clearly towardbetweenness centrality. The concept of betweenness centrality has beendescribed as the “rush” in a graph where the “rush” in an element is thetotal flow through the element, resulting from a flow between each pairof vertices. Betweenness centrality can be contrasted with other classicmeasures of centrality. Consider a minimal network of four peripheralnodes that are all connected to a single node in the middle (but not toeach other). There are at least three senses in which the node in themiddle is central. It is connected to a lot of nodes, relative to theothers, which is the notion of degree centrality. It is also verydirectly connected to all of the other nodes, whereas the peripheralnodes are at least two steps away from each other. This reflects thenotion of closeness centrality, usually measured as the average numberof steps required to reach other nodes in the network from a focal node.The middle node is also central in the sense that any kind of resourcesflowing in the network (meaning, in the case of CRA networks) must flowthrough it. This is the idea of “rush” or betweenness centralitydescribed above. Each of these measures can be computed for the networkas a whole, as well as for the individual words (or nodes).

[0037] Of the various kinds of centrality, betweenness centrality is themost appropriate for estimating the influence of words in CRA. Degreecentrality, the most often applied measure in earlier network textanalysis efforts, takes only the local connections of each word (ornode) into account. Closeness centrality is better in that it considersthe entire network structure. However, it cannot be computed fordisconnected graphs, which in CRA are not only possible but likely forlow-coherence texts. More important, closeness undervalues the influenceof words (or nodes) lying on paths connecting disparate parts of thenetwork because words (or nodes) in the center of large, denselyconnected clusters will have higher closeness, on the average. From thestandpoint of maintaining coherence in a structure of words, this“tying-together” function is crucial. Betweenness centrality thereforebest represents the extent to which a particular centering word(represented by a network node) mediates chains of association in theCRA network. It tells us how a given node channels the “rush” of meaningthrough a network of centering words. The influence I of a word i intext T is operationalized as:$I_{i}^{T} = \frac{\sum\limits_{j < k}{{g_{jk}(i)}/g_{jk}}}{\left\lbrack {\left( {N - 1} \right){\left( {N - 2} \right)/2}} \right\rbrack}$

[0038] where g_(jk) is the number of shortest paths connecting thej^(th) and k^(th) words, g_(jk)(i) is the number of those pathscontaining word i, and N is the number of words in the network.

[0039] Resonance is a latent property of the structure of a CRA network.While resonance is a property of a single network, it is only realizedin the presence of an external signal (i.e., another network), just as aphysical material only resonates when brought into contact with anexternal vibrating wave. To the extent that other texts or utterancesdeploy words in the same way as a given network, they may be said toresonate with it. To understand how the resonance of one text isoperational with another, assume that texts A and B have beenrepresented as CRA networks. The two texts may be of similar nature, orone may be considered a query and the other a text potentially relevantto the query. There are two ways of measuring resonance, one lessspecific and based on the words common in the two documents, the othermore specific and based on word pairs common in the two documents.

[0040] Word resonance is calculated directly from the influence scoresof the words in the two texts. For example, let the (unique) words(after parsing into phrases) for text A be represented by {W₁ ^(A), W₂^(A), . . . W_(N(A)) ^(A)) with corresponding influence scores of {I₁^(A), I₂ ^(A), . . . I_(N(A)) ^(A)}, where N(A) is the number of(unique) words in text A. Similarly, text B has words {W₁ ^(B), W₂ ^(B),. . . W_(N(B)) ^(B)) with influence scores {I₁ ^(B), I₂ ^(B), . . .I_(N(B)) ^(B)}. In general N(A)≠N(B). The indicator function α^(AB)_(ij) is equal to 1 if W_(i) ^(A) and W_(j) ^(B) are the same words, andthe indicator function is equal to zero if W_(i) ^(A) and W_(j) ^(B) arenot the same words. Accordingly, the word resonance between texts A andB, WR_(AB), is defined by:${WR}_{AB} = {\sum\limits_{i = 1}^{N{(A)}}{\sum\limits_{j = 1}^{N{(B)}}{I_{i}^{A} \cdot I_{j}^{B} \cdot \alpha_{ij}^{AB}}}}$

[0041] The more two texts frequently use the same words in influentialpositions, the more word resonance they have. The more word resonancethey have, the more the communicators used the same words, and the morethose words were prominent in structuring the text's coherence. Wordresonance is a more general measure of the mutual relevance of twotexts, and has applications in the modeling of large corpora.

[0042] This measure is unstandardized in the sense that resonance willincrease naturally as the two texts become longer in length and containmore common words. There are cases, however, where a standardizedmeasure is more appropriate. For example, in positioning documentsrelative to one another (as described below), one does not necessarilywant to overemphasize differences in document length, number of words,and so on. In these cases the appropriate standardized measure ofresonance is given by:${WR}_{AB}^{\prime} = {{WR}_{AB}/\sqrt{\sum\limits_{i = 1}^{N{(A)}}{\left( I_{i}^{A} \right)^{2} \cdot {\sum\limits_{j = 1}^{N{(B)}}\left( I_{j}^{B} \right)^{2}}}}}$

[0043] which is structurally equivalent to the manner in which thecovariance between two random variables is standardized to a measure ofcorrelation.

[0044] Pair resonance is estimated using co-occurring word-pairs, asopposed to co-occurring words. Let the frequency weighted pair influenceof words i and j in text T be given by:

P _(ij) ^(T) =I _(i) ^(T) ·I _(j) ^(T) ·F _(ij) ^(T)

[0045] where I_(i) ^(T) is the influence of W_(i) ^(T), I_(j) ^(T) isthe influence of W_(j) ^(T), and F_(ij) ^(T) is the number of times thatW_(i) ^(T) and W_(j) ^(T) co-occur (their corresponding nodes areconnected directly by an edge) in text T. If text T has N (unique)terms, then there will be (N·(N−1)/2) pairs, but many of them will havea value of F_(ij) ^(T)=0 as they will not represent connected terms. Letthe indicator function β^(AB) _(ijkl) be equal to 1 (a) if the two wordsets (W_(i) ^(A), W_(j) ^(A)) and (W_(k) ^(B), W_(l) ^(B)) areequivalent (regardless of the manner in which the set elements areordered), and (b) F_(ij) ^(A) and F_(kl) ^(B) both are equal to one (thesets represent connected nodes); otherwise the indicator is zero. Inother words, the indicator function β^(AB) _(ijkl) is 1 when thecorresponding pairs of co-occurring words co-occur in both texts. Thepair resonance PR_(AB) is defined by:${PR}_{AB} = {\sum\limits_{i = 1}^{{N{(A)}} - 1}\left( {\sum\limits_{j = {i + 1}}^{N{(A)}}\left( {\sum\limits_{k = 1}^{{N{(B)}} - 1}\left\lbrack {\sum\limits_{l = {k + 1}}^{N{(B)}}{P_{ij}^{A} \cdot P_{kl}^{B} \cdot \beta_{ijkl}^{AB}}} \right\rbrack} \right)} \right)}$

[0046] The more pair resonance two texts have, the more their authorsassembled words in the same ways, in order to make their communicationcoherent. Pair resonance is a more sensitive measure of the mutualrelevance of two texts than word resonance, because it takes account notonly of the words and their position in the network, but how they wereassembled in the utterances. Pair resonance has applications inhigh-accuracy information retrieval tasks.

[0047] For the same reasons discussed previously, it may be desirable toform a standardized measure of pair resonance. The standardized measureof pair resonance is as follows:${PR}_{AB}^{\prime} = {{{PR}_{AB}/\sqrt{\left( {\sum\limits_{i = 1}^{{N{(A)}} - 1}{\sum\limits_{j = {i + 1}}^{N{(A)}}\left( P_{ij}^{A} \right)^{2}}} \right)}} \cdot \sqrt{\left( {\sum\limits_{k = 1}^{{N{(B)}} - 1}{\sum\limits_{l = {k + 1}}^{N{(B)}}\left( P_{kl}^{B} \right)^{2}}} \right)}}$

[0048] Application

[0049] The final step in CRA is application, wherein the indexed CRAnetwork, or a set of indexed networks, is used for some analysis task.CRA networks are useful for a wide variety of tasks. One isvisualization of the CRA network for text understanding purposes. It ispossible to “read” a CRA network and get a good (though necessarilycompressed) sense of the content of the original text. In the nextsection entitled “CRA Applications” we illustrate how that can be donein one application. After that, another application known as spatialmodeling of resonance scores is described, which shows how CRA networkscan be used to analyze the intellectual organization of a set ofscholars. Other applications of CRA networks include, but are notlimited to, information retrieval and thematic analysis of collections.

[0050] In summary, CRA is a representational technique that describesthe extent to which words are prominent in creating a structural patternof coherence in a text. CRA possesses distinct advantages over othertext analysis approaches. First, because CRA networks are independent oftext corpora and training sets, they are highly transportable. Theinfluence values for words are calculated only once for a given text,and CRA networks can be computed for single texts, parts of texts, or asensible aggregation of texts. Second, because it does not depend ontraining or rules sets, CRA accommodates emergence of new terms orshifts in relationships among existing words and concepts, as should beexpected in knowledge development and other forms of innovation. Third,relative to other representational techniques, CRA is structurallysensitive in that it accounts for all likely chains of associationbetween the words that make texts and conversations coherent. This makesthe technique more sensitive to complex associations in the text thanstatistical methods based on word frequency or local co-occurrence.Fourth, CRA is based on a theory of communicative coherence that avoidsthe imposition of an arbitrary “window” sliding over text to locate wordco-occurrence.

CRA Applications

[0051] Application 1: Analyzing Group Interaction

[0052] To demonstrate the face validity of CRA network visualizations,an analysis of a transcribed videotape of organizational decision makingwas conducted. “After Mr. Sam” (Hammond & Pearson, 1974) is adocumentary film compiled from a long discussion, at a resort calledPalomino, by managers of Steinberg Limited, a Canadian retail chain. Themeeting takes place in the early 1970s, just before the founder and CEO,Sam Steinberg, appointed a successor and retired. In an early segment ofthe discussion, some managers argued that structural changes were neededfor the company before Mr. Steinberg's successor was appointed; othersargued against such structural changes. In FIG. 1, important statementssupporting the need for change (by a group called “The Advocates”) andopposing it (by “The Opponents”) are cited (in slightly edited form).FIGS. 2 and 3 display the CRA networks of those sets of statements (onefor the text in each column). FIG. 4 shows the CRA network derived fromthe whole relevant section of the transcript (acts 77 through 110, pp.6-9 of the transcript), including the arguments collected in the samplesfor the two groups.

[0053] The general procedures described above were followed in producingthe CRA networks. With CRA, pronoun disambiguation is decided on acase-by-case basis. In this analysis pronoun disambiguation wasperformed, substituting the word topteam for the pronoun “we,” in caseswhere the speaker clearly was using “we” to refer to the top managementcommittee as an empowered group. The disambiguation was appropriate inthis case because the discussion was focused on an issue of whether thetopteam group itself, or the new president would make key personneldecisions.

[0054] In the Advocates' network shown in FIG. 2, the most influentialand most frequently appearing concept is topteam. It is linked to otherinfluential concepts, like individual, nerve, and recommendation, thethree next-most influential words. As the graph shows, these words arein turn linked to other influential words including point, gumption,company, and haphazard. A look at FIG. 1 shows why topteam isinfluential: It is linked to a number of other words in varioussentences, and these other words get some of their influence by beinglinked directly to topteam. The pattern in FIG. 2 clearly represents thefocus of the Advocates' arguments that topteam has the duty (establishedearlier in meetings of this group) to make change recommendations, butis lacking the gumption to do this, and is approaching the task in ahaphazard way.

[0055]FIG. 3 represents the arguments by the Opponents who do not wantto make any definite change recommendation. They emphasize theprerogative of the next president, in concert with future board chairmanSam Steinberg, to decide on changes himself. They believe he needs tohave freedom of choice to do so, much as the U.S. President can makeCabinet appointments. Consistent with this position, the influentialwords in this network include president, chairman, prerogative, andchoice. The word topteam is also influential in the Opponents' network,but achieves that influence through its place in arguments that topteamis in danger of usurping presidential choice and should be aware of thatdanger.

[0056]FIG. 4 exhibits a graph based on a larger text sample, about 3pages of transcript from which the Advocates' and Opponents' argumentswere sampled. The network is striking in showing the top words for eachgroup (topteam and president) as central, yet distinct foci of somewhatdifferent, yet connected sub-networks. In the upper portion topteam isthe anchor, and is connected with other high influence words individual,recommendation, and people. In the lower portion we find presidentconnected to job, present(—company), chairman, and choice.Interestingly, it is not the case that all words on the bottom werespoken only by opponents and vice versa; for example the chainpresident—present—company can by found in the Advocates' network inFIG. 1. The word recommendation is rather influential because bothgroups used it in ways that linked it to various other influentialconcepts, as they advocated quite different forms of recommendations.

[0057] Even though words in the two parts of the network were notexclusively spoken by one side or another, the top and bottom parts ofthe graph align pretty well with the arguments of the two sides. Theadvocates sought to link topteam with the responsibility to make arecommendation and not allow some haphazard process to govern changes.The opponents focused on the president's prerogative to choose thepeople who will do key jobs. Accordingly, that the overall graph conveysthe discursive division between the recommendation role of topteam onone hand, and the choice-making job of the president (and the chairman)on the other. This composite example is based on very limited samples oftexts. The best use of CRA occurs with much larger bodies of text, inwhich important concept linkages will be more frequently repeated.

[0058] Application 2: Positioning of Authors

[0059] Resonance, as described above, is a measure of the mutualrelevance of two texts based on their CRA networks. The more theyresonate, the more their CRA networks are similar, so computing scoresfor all pairs of texts in some set yields a similarity matrix. Given aset of objects and similarities between them, a number of useful spatialmodeling techniques can be applied to help organize the objects,highlighting important similarities and differences between them.Applying this idea to texts, one can characterize the conceptualstructure of the sources from which the texts were drawn. Spatialmodeling is a recognized procedure in communication studies foranalyzing the relationship between texts.

[0060] To illustrate the application of CRA in spatial modeling, its useis described for clustering university faculty in an interdisciplinaryresearch area. While traditionally most academic research occurs withinthe boundaries of departments, colleges, and disciplines, the majorityof “tough problems” that are important today are interdisciplinary. Thisrequires the university and its faculty to be able to identify expertsand possible cohorts across departmental and collegiate boundaries,especially as a means of exploiting shifting funding priorities.

[0061] Environmental health is an example of such an interdisciplinaryarea. Its issues require expertise from both the physical and socialsciences, and cut across numerous disciplinary boundaries. The Office ofthe Vice Provost for Research (OVPR) at a large university providedinformation about some 55 faculty in the university who OVPR believedmight have interest and expertise in environmental health. The facultyrepresented over 15 different departments and were distributed over fivedifferent colleges. It was the belief of OVPR that there was littleexisting interaction between these individuals outside theirdepartments, and that most were probably not aware of each other ortheir corresponding interests.

[0062] OVPR provided a database consisting of personal statements ofresearch interests, grant proposal summaries, and abstracts of publishedworks of the researchers. From this database, article titles, articleabstracts, personal statements, and abstracts of funding proposals wereused as evidence of each individual's manifest knowledge. In so doing,all works listed on the database were included, regardless of the dateof publication or the authorship position of the faculty member. Thesetexts do not exhaust any researcher's knowledge of a content area;however, the combination of these readily available public documentsprovides a legitimate source to help understand the professionalexpertise that any individual researcher is likely to claim. In the55-person database provided by OVPR, there was too little information onseven researchers for their records to be meaningfully analyzed. In somecases, article titles but not abstracts were available; in other cases,there were simply too few entries. Thus, the final set consisted of 48researchers. Using the technique described above, standardized wordresonance scores were computed for each faculty member and scoredagainst all the others.

[0063] A matrix of similarities between the 48 researchers was submittedto hierarchical cluster analysis using Ward's method for agglomeration.Results of this analysis are shown in FIG. 5. The tightest clusters areshown in the shallow brackets toward the left. These are graduallycombined into higher-order clusters as the linkage distance (similarity)criterion is relaxed, until that criterion is relaxed so far we are leftwith one large cluster. In interpreting these plots, one looks for cleanbreaks that yield a manageable number of distinct clusters. FIG. 5clearly shows two distinct groups that are not merged until the veryend. The top main cluster contains two clear subclusters that remainseparate until linkage distance is relaxed to over 20. In the bottommain cluster, four such subclusters are discernable at roughly the samelinkage distance.

[0064] To aid in visualization of these clusters, multidimensionalscaling (MDS) was applied to the similarity data. MDS is a descriptivetechnique that, like cluster analysis, starts with a set of distancesbetween objects. It uses an iterative procedure to produce asmaller-dimensional space that optimally represents the originaldistances. In this application, nonmetric multidimensional scaling wasused because it has a tendency to produce more interpretable spaces. Theprogram KYST2a (Kruskal & Wish, Multidimensional scaling, Beverly Hills,Calif.: Sage, 1978) was used to scale the distances. A two dimensionalrepresentation produced an adequate fit to the data (stress=0.17).

[0065] MDS produces a set of points in n-dimensional space as itsoutput. The cluster analysis described above reveals groupings of thescaled objects. FIG. 6 represents a hybrid, with the clusterssuperimposed on a scatter plot of the MDS results. It is apparent fromFIG. 6 that the results of the MDS and clustering procedure closelyagree. The main clusters are distinct in the space and the subclustersare spatially distinct within these. There is, however, somedisagreement between techniques. The right main cluster “bulges” intothe left main cluster somewhat along the horizontal axis. Within theright cluster, two members of the subcluster represented by smallcircles are separated from the bulk of their group. Still, since theoverall agreement between the two techniques is good, this is consideredto be a “clean” clustering.

[0066] To interpret the clusters, the vitas and CRA maps of researcherswere viewed for similarities among members of the (sub)clusters. Theclearest-cut distinction is between two main clusters, separated on thehorizontal axis. This axis is interpreted in hard-soft science-terms:Researchers toward the left, certainly in the left cluster, are physicalscience oriented, and studying or measuring small-scale chemical andbiological processes. Those toward the right are concerned with largerscale phenomena affecting humans and populations of humans.

[0067] Within the micro-science cluster on the left, there are twosubclusters. Researchers in one group clearly study cell-levelprocesses, especially DNA and genetic processes, and related disorders.This cluster is labeled Cellular Genetics. In the second subcluster, theresearchers are concerned with measurement and sensing issues, asapplied to physical, biological, and biochemical systems. This clusteris labeled Measurement of Physical and Biological Systems.

[0068] Within the cluster on the right, there are four subclusters. Inone there are researchers who study basic biological processes. Theypredominately do experimental studies using humans and animal models.This subcluster is labeled Experimental Biology and Biochemistry.Another group includes researchers interested in diseases, stressors,pathogens, and patterns in such dysfunctions. This cluster is labeledDiseases and Disorders. Next there is a large group that studiespsychology, sociology, and communication in individuals, families, andlarger social groups. Social Sciences is the label for this subcluster.Finally, there is a group of faculty whose work clearly centers onPublic Health.

[0069] Maps like these have heuristic value in that they provide a senseof how a group of authors is organized with respect to a common field ofknowledge or activity domain. The maps have practical applications aswell, and OVPR could use the map in FIG. 6 for practical purposes. Whatis the best interdisciplinary team to pursue a grant in environmentalhealth? A “breadth” strategy would dictate including members from eachcluster, to best tap diverse knowledge resources of the organization. A“depth” strategy might dictate focusing grant-getting efforts in teamsfrom particular clusters. A more general application would be to fillstructural holes in the organizational network by ensuring that membersof clusters know and have the opportunity to interact with theircluster-mates. Alternatively, cluster maps could be used to identifystructural holes that could be filled by hiring employees with therequisite attributes or skills.

[0070] There is a steadily increasing volume of rhetoric and masscommunication research seeking to analyze large volumes of text,especially on the Internet. Accordingly, quantitative techniques thatcan be applied by computer will look increasingly attractive. CRA iscapable of producing meaningful abstractions of news stories orrhetorical acts, representing their main concepts andinterrelationships. These can be compared to one another, and analyzedfor change over time. CRA has broad applications in both organizationalcommunication as well as other areas of communication research.

[0071] In the foregoing specification, the invention has been describedwith reference to specific embodiments. However, one of ordinary skillin the art appreciates that various modifications and changes can bemade without departing from the scope of the present invention as setforth in the claims below. Accordingly, the specification and thefigures showing various applications are to be regarded in anillustrative rather than a restrictive sense, and all such modificationsare intended to be included within the scope of the present invention.

1. A method for categorizing text comprising the steps of: dividing thetext into sentences; parsing the sentences into one or more nounphrases; converting words in the noun phrases into networks of wordrelationships; and analyzing the word relationship networks to determinethe influence of each word.
 2. The method of claim 1 wherein the step ofparsing the sentences into one or more noun phrases comprises the stepof substituting disambiguated nouns in place of pronouns which arerelated to text analysis.
 3. The method of claim 1 wherein the step ofparsing the sentences into one or more noun phrases further comprisesthe step of converting plural words to their singular form.
 4. Themethod of claim 1 wherein the step of converting the word or words intonetworks of relationships comprises the step of linking all sequentiallyoccurring noun phrases within a sentence.
 5. The method of claim 4wherein the step of converting the words into networks of wordrelationships further comprises the step of linking all possible pairsof words in the noun phrases having three or more words.
 6. The methodof claim 1 wherein the step of analyzing the word relationship networksto determine the influence of each word comprises the step ofdetermining influence by utilizing between centrality.
 7. The method ofclaim 1 wherein the step of analyzing the word relationship networks todetermine the influence of each word comprises the step of determininginfluence by utilizing the following formula:$I = \frac{{g_{jk}(i)}/g_{jk}}{\left\lbrack {\left( {N - 1} \right){\left( {N - 2} \right)/2}} \right\rbrack}$

where I is the influence of a word (i) in the text (T) where g_(jk) isthe number of shortest paths connecting the j^(th) and k^(th) words,g_(jk)(i) is the number of those paths containing word (i), and N is thenumber of words in the network.
 8. A method for analyzing textcomprising the steps of: dividing the text into sentences; parsing thesentences into one or more noun phrases; converting one or more wordswithin each of the noun phrases into networks of relationships betweenwords; analyzing the networks to determine the influence for each word;and applying the analyzed networks to perform a specific analysis task.9. The method of claim 8 wherein the step of parsing the sentences intoone or more noun phrases further comprises the step of substitutingdisambiguated nouns in place of pronouns which are relevant to textanalysis.
 10. The method of claim 8 wherein the step of parsing thesentences into one or more noun phrases further comprises the step ofconverting plural words to their singular form.
 11. The method of claim8 wherein the step of converting the word or words into networks ofrelationships comprises the step of linking all sequentially occurringnoun phrases within a sentence.
 12. The method of claim 11 wherein thestep of converting the words into networks of relationships betweenwords further comprises the step of linking all possible pairs of wordsin the noun phrases having three or more words.
 13. The method of claim8 wherein the step of analyzing the networks to determine the influenceof each word comprises the step of determining influence by utilizingbetween centrality.
 14. The method of claim 8 wherein the step ofanalyzing the networks to determine the influence of each word comprisesthe step of determining influence by utilizing the following formula:$I = \frac{{g_{jk}(i)}/g_{jk}}{\left\lbrack {\left( {N - 1} \right){\left( {N - 2} \right)/2}} \right\rbrack}$

where I is the influence of a word (i) in the text (T) where g_(jk) isthe number of shortest paths connecting the j^(th) and k^(th) words,g_(jk)(i) is the number of those paths containing word (i), and N is thenumber of words in the network.
 15. The method of claim 8 where the stepof applying the analyzed network to perform a specific analysis taskcomprises the step of applying the analyzed network to perform at leastone of visualization of the network to understand text, spatial modelingof resonance scores, information retrieval, and thematic analysis ofcollections.
 16. A method for determining resonance based on commonwords in two sets of text comprising the step of utilizing the followingformula:${WR}_{AB} = {\sum\limits_{i = 1}^{N{(A)}}{\sum\limits_{j = 1}^{N{(B)}}{I_{i}^{A} \cdot I_{j}^{B} \cdot \alpha_{ij}^{AB}}}}$

where WR_(AB) is the word resonance between texts A and B, {W₁ ^(A), W₂^(A), W_(N(A)) ^(A)) are unique words for text A after parsing intophrases in accordance with claim 8 where N(A) is the number of uniquewords in text A, {I₁ ^(A), I₂ ^(A), . . . I_(N(A)) ^(A)} are influencescores calculated in accordance with claim 14 for the unique words intext A, {W₁ ^(B), W₂ ^(B), . . . W_(N(B)) ^(B)) are unique words fortext B after parsing into phrases in accordance with claim 8 where N(B)is the number of unique words in text B, {I₁ ^(B), I₂ ^(B), . . .I_(N(B)) ^(B)} are influence scores calculated in accordance with claim14 for the unique words in text B. and indicator function α^(AB) _(ij)is equal to 1 if W_(i) ^(A) and W_(j) ^(B) are the same words, and theindicator function is equal to zero if W_(i) ^(A) and W_(j) ^(B) are notthe same words.
 17. The method of claim 16 further comprising the stepof determining standardized resonance based on common words in texts Aand B comprising the step of utilizing the following formula:${WR}_{AB}^{\prime} = {{WR}_{AB}/\sqrt{\sum\limits_{i = 1}^{N{(A)}}{\left( I_{i}^{A} \right)^{2} \cdot {\sum\limits_{j = 1}^{N{(B)}}\left( I_{j}^{B} \right)^{2}}}}}$

where WR_(AB)′ is the standardized word resonance between texts A and B,WR_(AB) is the actual word resonance between texts A and B,$\sum\limits_{i = 1}^{N{(A)}}\left( I_{i}^{A} \right)^{2}$

 is the sum of all influence scores for the unique words in text Asquared, and$\sum\limits_{j = 1}^{N{(B)}}\left( I_{j}^{B} \right)^{2}$

 is the sum of all influence scores for the unique words in text Bsquared.
 18. A method for determining pair resonance based on commonword pairs in two sets of text comprising the step of utilizing thefollowing formula, where influence is calculated according to claim 14:${PR}_{AB} = {\sum\limits_{i = 1}^{{N{(A)}} - 1}\left( {\sum\limits_{j = {i + 1}}^{N{(A)}}\left( {\sum\limits_{k = 1}^{{N{(B)}} - 1}\left\lbrack {\sum\limits_{l = {k + 1}}^{N{(B)}}{P_{ij}^{A} \cdot P_{kl}^{B} \cdot \beta_{ijkl}^{AB}}} \right\rbrack} \right)} \right)}$

where PR_(AB) is the word pair resonance between texts A and B, P_(ij)^(A) is the frequency weighted pair influence of words i and j in text Aand is equal to I_(i) ^(A)·I_(j) ^(A)·F_(ij) ^(A) where F_(ij) ^(A) isthe number of times that W_(i) ^(A) and W_(j) ^(A) co-occur in text A,P_(ij) ^(D) is the frequency weighted pair influence of words k and l intext B and is equal to I_(k) ^(B)·I_(l) ^(B)·F_(kl) ^(B) where F_(kl)^(B) is the number of times that W_(k) ^(B) and W_(l) ^(B) co-occur intext B, and indicator function β_(ijkl) ^(AB) is equal to 1 if the twoword sets (W_(i) ^(A), W_(j) ^(A)) and (W_(k) ^(B), W_(l) ^(B)) areequivalent and if F_(ij) ^(A) and F_(kl) ^(B) both are equal to one,otherwise the indicator is zero.
 19. The method of claim 18 furthercomprising the step of determining standardized resonance based oncommon word pairs in texts A and B comprising the step of utilizing thefollowing formula:${PR}_{AB}^{\prime} = {{{PR}_{AB}/\sqrt{\left( {\sum\limits_{i = 1}^{{N{(A)}} - 1}{\sum\limits_{j = {i + 1}}^{N{(A)}}\left( P_{ij}^{A} \right)^{2}}} \right)}} \cdot \sqrt{\left( {\sum\limits_{k = 1}^{{N{(B)}} - 1}{\sum\limits_{l = {k + 1}}^{N{(B)}}\left( P_{kl}^{B} \right)^{2}}} \right)}}$

where PR′_(AB) is the standardized word pair resonance between texts Aand B and PR_(AB) is the actual word pair resonance between texts A andB.
 20. A method for searching two or more texts utilizing resonancescores obtained in accordance with claim
 16. 21. A method for searchingtwo or more texts utilizing resonance scores obtained in accordance withclaim
 17. 22. A method for searching two or more texts utilizingresonance scores obtained in accordance with claim
 18. 23. A method forsearching two or more texts utilizing resonance scores obtained inaccordance with claim
 19. 24. A method for modeling two or more textsutilizing resonance scores obtained in accordance with claim
 16. 25. Amethod for modeling two or more texts utilizing resonance scoresobtained in accordance with claim
 17. 26. A method for modeling two ormore texts utilizing resonance scores obtained in accordance with claim18.
 27. A method for modeling two or more texts utilizing resonancescores obtained in accordance with claim
 19. 28. A method for analyzingtext comprising the steps of: a) compartmentalizing the text intodefined units; b) categorizing the defined units by: parsing the unitsinto one or more noun phrases each comprising one or more words;converting the word or words into networks of relationships betweenwords; analyzing the networks of word associations to determine thestructural influence of each word; and c) applying the analyzed networkto perform a specific analysis task.
 29. The method of claim 28 whereinthe step of compartmentalizing the text into defined units comprises thestep of breaking down the text into sentences.
 30. The method of claim28 wherein the step of parsing the units into one or more noun phrasesfurther comprises the step of substituting disambiguated nouns in placeof pronouns which are relevant to text analysis.
 31. The method of claim28 wherein the step of parsing the units into one or more noun phrasesfurther comprises the step of converting plural words to their singularform.
 32. The method of claim 28 wherein the step of converting the wordor words into networks of relationships comprises the step of linkingall sequentially occurring noun phrases within a defined unit.
 33. Themethod of claim 32 wherein the step of converting the word or words intonetworks of relationships further comprises the step of linking allpossible pairs of words in those noun phrases having three or morewords.
 34. The method of claim 28 wherein the step of analyzing thenetwork to determine the structural influence of each word comprises thestep of determining structural influence by utilizing betweenesscentrality.
 35. The method of claim 28 wherein the step of analyzing thenetwork to determine the structural influence of each word comprises thestep of determining structural influence by utilizing the followingformula:$I = \frac{{g_{jk}(i)}/g_{jk}}{\left\lbrack {\left( {N - 1} \right){\left( {N - 2} \right)/2}} \right\rbrack}$

where I is the influence of a word (i) in the text (T) where g_(jk) isthe number of shortest paths connecting the j^(th) and k^(th) words,g_(jk)(i) is the number of those paths containing word (i), and N is thenumber of words in the network.
 36. The method of claim 1 where the stepof applying the analyzed network to perform a specific analysis taskcomprises the step of applying the analyzed network to perform at leastone of visualization of the network to understand text, spatial modelingof resonance scores, information retrieval, and thematic analysis ofcollections.
 37. A method for determining resonance based on commonwords in two sets of text comprising the step of utilizing the followingformula:${WR}_{AB} = {\sum\limits_{i = 1}^{N{(A)}}{\sum\limits_{j = 1}^{N{(B)}}{I_{i}^{A} \cdot I_{j}^{B} \cdot \alpha_{ij}^{AB}}}}$

where WR_(AB) is the word resonance between texts A and B, {W₁ ^(A), W₂^(A), . . . W_(N(A)) ^(A)) are unique words for text A after parsinginto phrases in accordance with claim 28 where N(A) is the number ofunique words in text A, {I₁ ^(A), I₂ ^(A), . . . I_(N(A)) ^(A)} areinfluence scores calculated in accordance with claim 35 for the uniquewords in text A, {W₁ ^(B), W₂ ^(B), W_(N(B)) ^(B)) are unique words fortext B after parsing into phrases in accordance with claim 28 where N(B)is the number of unique words in text B, {I₁ ^(B), I₂ ^(B), . . .I_(N(B)) ^(B)} are influence scores calculated in accordance with claim35 for the unique words in text B. and indicator function α^(AB) _(ij)is equal to 1 if W_(i) ^(A) and W_(j) ^(B) are the same words, and theindicator function is equal to zero if W_(i) ^(A) and W_(j) ^(B) are notthe same words.
 38. The method of claim 37 further comprising the stepof determining standardized resonance based on common words in texts Aand B comprising the step of utilizing the following formula:${WR}_{AB}^{\prime} = {{WR}_{AB}/\sqrt{\sum\limits_{i = 1}^{N{(A)}}{\left( I_{i}^{A} \right)^{2} \cdot {\sum\limits_{j = 1}^{N{(B)}}\left( I_{j}^{B} \right)^{2}}}}}$

where WR_(AB)′ is the standardized word resonance between texts A and B,WR_(AB) is the actual word resonance between texts A and B,$\sum\limits_{i = 1}^{N{(A)}}\left( I_{i}^{A} \right)^{2}$

 the sum of all influence scores for the unique words in text A squared,and $\sum\limits_{j = 1}^{N{(B)}}\left( I_{j}^{B} \right)^{2}$

 is the sum of all influence scores for the unique words in text Bsquared.
 39. A method for determining pair resonance based on commonword pairs in two sets of text comprising the step of utilizing thefollowing formula, where influence is calculated in accordance withclaim 35:${PR}_{AB} = {\sum\limits_{i = 1}^{{N{(A)}} - 1}\left( {\sum\limits_{j = {i + 1}}^{N{(A)}}\left( {\sum\limits_{k = 1}^{{N{(B)}} - 1}\left\lbrack {\sum\limits_{l = {k + 1}}^{N{(B)}}{P_{ij}^{A} \cdot P_{kl}^{B} \cdot \beta_{ijkl}^{AB}}} \right\rbrack} \right)} \right)}$

where PR_(AB) is the word pair resonance between texts A and B, P_(ij)^(A) is the frequency weighted pair influence of words i and j in text Aand is equal to I_(i) ^(A)·I_(j) ^(A)·F_(ij) ^(A) where F_(ij) ^(A) isthe number of times that W_(i) ^(A) and W_(j) ^(A) co-occur in text A,P_(ij) ^(B) is the frequency weighted pair influence of words k and l intext B and is equal to I_(k) ^(B)·I_(l) ^(B)·F_(kl) ^(B) where F_(kl)^(B) is the number of times that W_(k) ^(B) and W_(l) ^(B) co-occur intext B, and indicator function β_(ijkl) ^(AB) is equal to 1 if the twoword sets (W_(i) ^(A), W_(j) ^(A)) and (W_(k) ^(B), W_(l) ^(B)) areequivalent and if F_(ij) _(A) and F_(kl) ^(B) both are equal to one,otherwise the indicator is zero.
 40. The method of claim 39 furthercomprising the step of determining standardized resonance based oncommon word pairs in texts A and B comprising the step of utilizing thefollowing formula:${PR}_{AB}^{\prime} = {{{PR}_{AB}/\sqrt{\left( {\sum\limits_{i = 1}^{{N{(A)}} - 1}{\sum\limits_{j = {i + 1}}^{N{(A)}}\left( P_{ij}^{A} \right)^{2}}} \right)}} \cdot \sqrt{\left( {\sum\limits_{k = 1}^{{N{(B)}} - 1}{\sum\limits_{l = {k + 1}}^{N{(B)}}\left( P_{kl}^{B} \right)^{2}}} \right)}}$

where PR′_(AB) is the standardized word pair resonance between texts Aand B and PR_(AB) is the actual word pair resonance between texts A andB.
 41. A method for searching two or more texts utilizing resonancescores obtained in accordance with claim
 37. 42. A method for searchingtwo or more texts utilizing resonance scores obtained in accordance withclaim
 38. 43. A method for searching two or more texts utilizingresonance scores obtained in accordance with claim
 39. 44. A method forsearching two or more texts utilizing resonance scores obtained inaccordance with claim
 40. 45. A method for modeling two or more textsutilizing resonance scores obtained in accordance with claim
 37. 46. Amethod for modeling two or more texts utilizing resonance scoresobtained in accordance with claim
 38. 47. A method for modeling two ormore texts utilizing resonance scores obtained in accordance with claim39.
 48. A method for modeling two or more texts utilizing resonancescores obtained in accordance with claim 40.